我是Tensorflow的新手。我想构建一个简单的模型，但是我遇到了这个错误。我认为这是因为标签的问题，但我不知道如何解决。我通过目录文件使用tf.data.Dataset构建了我的数据集。
这是数据集：
访问 : https://i.sstatic.net/H2EQT.jpg
数据
- ——–class1: [x.jpeg …]
- ——–class2: [y.png ….]
- .
- .
- .
- ——–class10: [z.jpg …]
data_dir = os.path.join(os.path.dirname('D:/Downloads/Image data set/'), 'raw-img') data_dir = pathlib.Path(data_dir) image_count = len(list(data_dir.glob('*/*.*'))) list_ds = tf.data.Dataset.list_files(str(data_dir / '*/*'), shuffle=False) list_ds = list_ds.shuffle(image_count, reshuffle_each_iteration=False) train_size = int(image_count * 0.8) test_size = int(image_count * 0.1) val_size = int(image_count * 0.1) train_ds = list_ds.take(train_size) val_ds = list_ds.skip(train_size) test_ds = val_ds.skip(test_size) val_ds = val_ds.take(test_size) def parse_image(filename): parts = tf.strings.split(filename, os.sep) label = tf.cast(parts[-2] == class_names, tf.float32) label = tf.argmax(label) image = tf.io.read_file(filename) image = tf.image.decode_jpeg(image, channels=3) image = tf.image.convert_image_dtype(image, tf.float32) image = tf.reshape(image, [32, 150, 150, 3]) return image, labels AUTOTUNE = tf.data.experimental.AUTOTUNE train_ds = train_ds.map(parse_image, num_parallel_calls=AUTOTUNE) val_ds = val_ds.map(parse_image, num_parallel_calls=AUTOTUNE) test_ds = test_ds.map(parse_image, num_parallel_calls=AUTOTUNE) model = tf.keras.Sequential([ kr.layers.Conv2D(16, 3, activation='relu', input_shape=(150, 150 ,3)), kr.layers.MaxPooling2D(), kr.layers.Conv2D(32, 3, activation='relu'), kr.layers.MaxPooling2D(), kr.layers.Conv2D(64, 3, activation='relu'), kr.layers.MaxPooling2D(), kr.layers.Flatten(), kr.layers.Dense(128, activation='softmax'), kr.layers.Dense(10) ]) model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit( train_ds, epochs=3 )错误：当我尝试拟合模型时出现了这个错误。
Train for 20943 stepsEpoch 1/3 1/20943 [..............................] - ETA: 1:14:41---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-38-c408313d0649> in <module> 1 model.fit( 2 train_ds,----> 3 epochs=3 4 ) ....ValueError: Shape mismatch: The shape of labels (received (320,)) should equal the shape of logits except for the last dimension (received (32, 10)).回答：
问题通过使用批量大小和每轮步骤变量解决了。
train_ds = train_ds.map(parse_image, num_parallel_calls=AUTOTUNE)train_ds = train_ds.cache()train_ds = train_ds.batch(BATCH_SIZE, drop_remainder=True).repeat()train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)然后训练
history = model.fit(train_ds, steps_per_epoch = train_size // BATCH_SIZE, epochs = EPOCHS, batch_size = BATCH_SIZE, validation_data = val_ds, validation_steps = val_size // BATCH_SIZE)问题解决了。
